The purpose of all manufacturing processes is to manufacture a part that meets performance expectations while doing so at the lowest possible cost. There are many varied factors that affected the cost of a part. To name a few, there are costs associated with material, labor, fabrication steps, testing, and yield. The content of this invention addresses the cost that is associated with yield. When a part is rejected from a process, unless the part can be reworked, all the cost associated with fabricating the part up to the point of rejection is thrown away with the part. The cost associated with reworking the part also accumulates in the total part cost. The parts that leave a process bear the cost associated with rejected and reworked parts.
In the manufacturing community there are many methods and techniques that are well known in the art for controlling the yields of a manufacturing process and consequently the cost associated with yield. Copious amounts of data are collected on measured parameters of components fabricated in manufacturing processes in attempts to control yields and cost. Most techniques practiced in industry today focus on predicting and controlling trends in a manufacturing process. Some of these techniques are described briefly below. The following descriptions are presented only as an overview of process control techniques, and are not intended to be an exhaustive listing of all process control techniques.
A Process Control Chart (PCC) 100, referring to FIG. 1, is used to track trend 110 of measured parameters of parts being fabricated in a process. The averages 115 of a measured parameter for a run or batch 112 of parts are plotted as a trend 110 as they are processed. FIG. 1 shows 9 multiples of batch 112 processed and measured. Trend 110 is compared to predefined upper and lower control limits (heron referred to as UCL 120 and LCL 125 respectively). The UCL 120 and LCL 125 are determined from evaluating the process's capability for meeting the specified parameter. UCL 120 and LCL 125 are usually defined as the standard deviation of the measured parameter after the fabrication process has been optimized. UCL 120 and LCL 125 are more restrictive than upper specified tolerance 130 and lower specified tolerance 135 for the specified parameter of the part. The fabrication process is monitored more closely when average 115 of a measured parameter of a batch 112 begins to track too closely to either UCL 120 or LCL 125, such as demonstrated by average data points 10 and 20. Average data points 30 and 40 demonstrate batches of the fabrication process that are out of control, at which time the fabrication process is stopped and adjusted. The fabrication process can also be adjusted so that trend 110 meets an average target value if performance of the population of parts needs to be adjusted.
Referring to FIG. 2, plot 200 represents a concept of Statistical Process Control (SPC). It is similar in function as PCC 100. SPC 200 tracks the distribution 210 of a measured parameter of a population of parts and compares the distribution to the target mean value (or specified parameter 220) and the specified tolerance values. The specified tolerance value usually consists of an upper specified limit (USL 230) and a lower specified limit (LSL 235). Specified parameter 220 is typically the mean value 205 of the measured parameter of the part. There are variations in specification techniques that specify tolerances with values other than USL 230 and LSL 235. Discussion of these specification techniques is beyond the scope of this discussion. These specification techniques for tolerance specification can usually be translated into a USL 230 and a LSL 235. A manufacturer of a part will use SPC 200 to adjust a process to produce parts closer to specified parameter 220. Adjusting a process to produce parts with measured parameters closer to specified parameter 220 will produce more parts that are in the acceptable range 215. A manufacturer may also choose to discard parts whose measured parameters exceed USL 230 or LSL 235 and are in the unacceptable range 240.
Referring to FIG. 2, a specification technique is used in an attempt to control the shape of distribution 210. This specification technique specifies terms known as Cp and Cpk of distribution 210. Without presenting a detailed explanation of Cp and Cpk, in short, Cp specifies width 255 of acceptable range 215. Cpk specifies range 250 that specified mean value 205 can vary within.
The aforementioned process control techniques are exemplary of techniques used in industry for controlling yields of manufactured parts. These examples are not an exhaustive list of all process control techniques. Inclusion or omission of a process control technique does not limit the embodied invention. One schooled in the art will recognize that there are many other examples of process control techniques.
All of these techniques control trends and distributions in the populations of parts in attempts to control the yields. Parts that have parameters specified with these control techniques assume that if the specified target, tolerance values, and the distribution of the population for the specified parameter are met, then the part will function properly. Assuring function in this manner can have unnecessary associated cost for controlling yield. Using the criteria of specified target and tolerance values, and specifying the shape of a distribution plot can lead to throwing away parts unnecessarily and increase the overall cost to fabricate a part.